Mikä on klikkiotsikko?

Mikä on klikkiotsikko?

Lyhykäisyydessään klikkiotsikko tai klikkiansa (eng. clickbait) viittaa otsikkoon sekä siihen liittyvään verkkosisältöön, joka on harhaanjohtava ja houkuttelee klikkaamaan. Klikkiotsikon tarkoituksena on usein kerryttää klikkausten avulla mainostuloja. Periaatteena on, että mitä enemmän klikkejä, sitä enemmän mainostuloja.

Miksi klikkiotsikot toimivat?

Klikkiotsikko herättää tyypillisesti mielenkiinnon vetoamalla tunteisiin. Tavallisesti ihmiset klikkaavat negatiivisesti sävyttyneisiin otsikoihin enemmän kuin positiivisiin. Tämän ovat myös klikkiotsikoiden tekijät huomanneet, ja siksi monesti otsikot vaikuttavat tunnetasolla lähinnä kielteisellä tavalla. Klikkiotsikoiden tärkein missio on siis herättää tunteita ja saada klikkaamaan.

Kuinka tunnistaa klikkotsikko?

Klikkiotsikon tunnistaa tyypillisesti sensaatiohakuisesta tyylistä. Usein otsikko viestii jostakin hätkähdyttävästä ja rajoja rikkovasta asiasta, joka ilmeisesti olisi syytä tietää. Monesti lukija kuitenkin pettyy, kun lupauksia tarjonnut otsikko ei johdakaan mihinkään elämää mullistavaan tietoon. Sen sijaan otsikon takaa löytyvä verkkosisältö voi olla täysin päinvastaista kuin otsikon väittämä. Otsikkoon klikkaaminen johtaa esimerkiksi uutisartikkeliin, joka sisältää syvällisen tiedon sijaan lukemista häiritseviä mainoksia taikka muuta sisältöä kuin otsikossa on luvattu. Todellista informaatioarvoa ei välttämättä ole lainkaan.

Klikkiotsikot luovat epäluottamusta

Klikkiotsikoita saatetaan klikata jonkun aikaa, mutta ennemmin tai myöhemmin ihmiset eivät luota enää tiedonlähteeseen. Pettyminen laaduttomaan ja harhaanjohtavaan sisältöön johtaa lopulta siihen, ettei otsikoita edes klikata. Informaatioalustoille, kuten Google ja Facebook, klikkiotsikot edustavat siis riskiä käyttäjien huomion menettämisestä. Tämän vuoksi esimerkiksi Facebook on leikannut klikkiotsikoilta vaikuttavan sisällön näkyvyyttä.

Kohti parempaa uutisointia

Klikkiotsikot ovat herättäneet paljon keskustelua ja Suomessakin on niiden pohjalta perustettu klikkijournalismin vähentämiseksi esimerkiksi Klikinsäästäjä (https://klikinsaastaja.fi/).

On Social Media Sampling

In social media sampling, there are many issues. Two of them are: 1) the silent majority problem and 2) the grouping problem.

The former refers to the imbalance between participants and spectators: can we trust that the vocal few represent the views of all?

The latter means that people of similar opinions tend to flock together, meaning that looking at one online community or even social media platform we can get a biased understanding of the whole population.

Solving these problems is hard, and requires understanding of the online communities, their polarity, sociology and psychology driving the participation, and the functional principles of the algorithms that determine visibility and participation in the platforms.

Prior knowledge on the online communities can be used as a basis for stratified sampling that can be a partial remedy.

Web 3.0: The dark side of social media

Web 2.0 was about all the pretty, shiny things about social media, like user-generated content, blogs, customer participation, ”everyone has a voice,” etc. Now, Web 3.0 is all about the dark side: algorithmic bias, filter bubbles, group polarization, flame wars, cyberbullying, etc. We discovered that maybe everyone should not have a voice, after all. Or at least that voice should be used with more attention to what you are saying.

While it is tempting to blame Facebook, media, or ”technology” for all this (just as it is easy to praise it for the other things), the truth is that individuals should accept more responsibility of their own behavior. Technology provides platforms for communication and information, but it does not generate communication and information; people do.

In consequence, I’m very skeptical about technological solutions to the Web 3.0 problems; they seem not to be technological problems but social ones, requiring primarily social solutions and secondly hybrid solutions. We should start respecting the opinions of others, get educated about different views, and learn how to debate based on facts and finding fundamental differences, not resorting to argumentation errors. Here, machines have only limited power – it’s up to us to re-learn these things and keep teaching them to new generations. It’s quite pitiful that even though our technology is 1000x better than in Ancient Greek, our ability to debate properly is one tenth of what it was 2000 years ago.

Avoiding the enslavement of machines requires going back to the basics of humanity.

Machine decision making and workflow engineering

Did you ever want to climb Mount Everest?

If you did, you would have to split such a goal into many tasks: You would first need to find out what resources are needed for it, who could help you, how to prepare mentally and physically, etc. You would come up with a list of tasks that, in a sequence, form your plan of achieving the goal.

The same logic applies to all goals we humans have, both in companies and private lives, and it also applies when evaluting what tasks, given a goal, can be outsourced to machine decision making.

The best to way to conduct such an analysis is to view organizational goals as a sequence of inter-related job tasks, and then evaluate which particular sub-tasks humans are best at handling, and vice versa.

  1. Define the end goal (e.g., launch a marketing campaign)
  2. Define the steps needed to achieve that goal (strategy) (e.g., decide targeting, write ads, define budget, optimize spend)
  3. Divide each step into sub-tasks (e.g., decide targeting: analyze past campaigns, analyze needs from social media)
  4. Evaluate (e.g., on a scale of 1-5) how well machine and human perform in each sub-task (e.g., write ads: human = 5, machine = 1)
  5. Look at the entire chain and identify points of synergy (where machine can be used to enhance human work or vice versa (e.g., analyze social media by supervised machine learning where crowd workers tag tweets).

We find, by applying such logic, that there are plenty of such tasks in organizational workflows that currently cannot be outsourced to machines, out of variety of reasons. Sometimes the reasons relate to manual processes, i.e. the overall context does not support optimal carrying out of tasks. An example: currently, I’m manually downloading receipts from a digital marketing service account => I have to manually log-in and retrieve the receipts as PDF files, and then send them as email attachment to book-keeping. Ideally, the book-keeping system would just retrieve the receipts via an application programming interface (API) automatically, eliminating this unnecessary part of human labor.

At the same time, we should a) work to remove unnecessary barrier to work automation where it is feasible, b) while thinking of ways to provide optimal synergy from human and machine work inputs. This is not about optimizing individual work tasks, but optimizing the entire workflows toward reaching a specific goal. At the moment, there is little research and attention paid to this kind of comprehensive planning, which I call ”workflow engineering”.

From polarity to diversity of opinions

The problem with online discussions and communities is that the extreme poles draw people effectively, causing group polarization in which the original opinion of a person becomes more radical due to influence of the group. In Finnish, we have a saying ”In a group, stupidity concentrates” (joukossa tyhmyys tiivistyy).

Here, I’m exploring the idea that this effect, namely the growth of polar extremes (for example, being for or against immigration, as currently many European citizens are) is simply because people are lacking options to identify with. There are only the extremes, but no neutral or moderate group, even though, as I’m arguing here, most people in fact are moderate and understand that extremes and absolutes are misleading simplifications either way.

In other words, when there are only two ”camps” of opinion, people are more easily split between them. However, my argument is that people have preferences that correspond to being in the middle, not in the extremes.

These preferences remain hidden because there are only two camps to subscribe to: One cannot be moderate because there is no moderate group.

For example, there are liberals and conservatives, but what about the people in the middle? What about them who share some ideas of liberals and others from conservatives? By having only these two groups, other combinations become socially impossible because people are, again socially, pressed to observe all the opinions of the group they’re subscribing to, even if they wouldn’t agree with a particular view. This effect has been studied in relation to the concept of groupthink, but no permanent remedy has been found.

How to solve the problem of extremes?

My idea is simple: we should start more camps, more views to subscribe to, especially those representing moderate views.

The argument is that having more supply of camps, people will distribute more evenly between them and we have less polarization as a consequence.

This is illustrated in the picture (sketched quickly in Paint since I got an inspiration).

a and b

In (A), public discourse is dominated by the extremes (the distribution of attention is skewed toward the extremes of a given opinion spectrum). In (B), the distribution is focused on the center of the opinion spectrum (=moderate views) while the extremes are marginalized (as they should be, according to the assumption of moderate majority).

An example: having several political parties results in more diverse views being presented. In the US, you are either a Democrat or a Republican (although there are  marginal Green Party and the progressives, it must be stated), but in Finland you can also be many others: Center Party, National Coalition Party, or Green Party, for example. The same applies to most countries in Europe. Although I don’t have facts for this, it seems that the public discourse in the US is exceptionally polarized compared to many other countries [1].

Giving more choices to identify with for the ”silent majority” that is moderate rather than extreme, revealing the ”true” opinions of citizens, would ideally marginalize both extremes, avoiding the tyrannity of minority [2] currently dominating the public discourse.

Finally, all this could be formalized in game theory by assuming heterogeneity of preferences over the opinion spectrum and parameters such as gravity (”pull factor” by the extremes), justifiable e.g. by media attention given to extreme views over moderate ones. But the implication reains the same: diversity of classes reduces polarization under the set of assumptions.

Footnotes

[1] Of course there are other reasons, such as media taking political sides.

[2] This means extreme views are not representative to the whole population (which is more moderate than either view) but they get disproportionate attention in the media and public discourse. This is because the majority views are hidden; they would need to be revealed.

How to teach machines common sense? Solutions for the ambiguity problem of AI

Introduction

The ambiguity problem illustrated:

User: ”Siri, call me an ambulance!”

Siri: ”Okay, I will call you ’an ambulance’.”

You’ll never reach the hospital, and end up bleeding to death.

Solutions

Two potential solutions come to mind:

A. machine builds general knowledge (”common sense”)

B. machine identifies ambiguity & asks for clarification from humans (reinforcement learning)

The whole ”common sense” problem can be solved by introducing human feedback into the system. We really need to tell the machine what is what, just like a child. This is iterative learning, in which trials and errors take place. However, it is better than trying to adapt an unescapably finite dataset into a close to finite space of meanings.

But, in fact, A and B converge by doing so. Which is fine, and ultimately needed.

Contextual awareness

To determine which solution to an ambiguous situation is proper, the machine needs contextual awareness; this can be achieved by storing contextual information from each ambiguous situation, and being explained ”why” a particular piece of information results in disambiguity. It’s not enough to say ”you’re wrong”, but there needs to be an explicit association to a reason (concept, variable). Equally, it’s not enough to say ”you’re right”, but again the same association is needed.

The process:

1) try something

2) get told it’s not right, and why (linking to contextual information)

3) try something else, corresponding to why

4) get rewarded, if it’s right.

The problem is, currently machines are being trained by data, not by human feedback.

New thinking: Training AI pets

So we would need to build machine-training systems which enable training by direct human feedback, i.e. a new way to teach and communicate with the machine. It’s not a trivial thing, since the whole machine-learning paradigm is based on data, not meanings. From data and probabilities, we would need to move into associations and concepts that capture social reality. A new methodology is needed. Potentially, individuals could train their own AIs like pets (think having your own ”AI pet” like Tamagotchi), or we could use large numbers of crowd workers who would explain the machine why things are how they are (i.e., create associations). A specific type of markup (=communication with the machine) would probably also be needed, although conversational UIs would most likely be the best solution.

Through mimicking human learning we can teach the machine common sense. This is probably the only way; since common sense does not exist beyond human cognition, it can only be learnt from humans. An argument can be made that this is like going back in time, to era where machines followed rule-based programming (as opposed to being data-driven). However, I would argue rule-based learning is much closer to human learning than the current probability-based one, and if we want to teach common sense, we therefore need to adopt the human way.

Conclusion

Machine learning may be at par, but machine training certainly is not. The current machine learning paradigm is data-driven, whereas we could look into ways for concept-driven AI training approaches. Essentially, this is something like reinforcement learning for concept maps.

The black sheep problem in machine learning

Introduction. Hal Daumé wrote an interesting blog post about language bias and the black sheep problem. In the post, he defines the problem as follows:

The ”black sheep problem” is that if you were to try to guess what color most sheep were by looking and language data, it would be very difficult for you to conclude that they weren’t almost all black. In English, ”black sheep” outnumbers ”white sheep” about 25:1 (many ”black sheep”s are movie references); in French it’s 3:1; in German it’s 12:1. Some languages get it right; in Korean it’s 1:1.5 in favor of white sheep. This happens with other pairs, too; for example ”white cloud” versus ”red cloud.” In English, red cloud wins 1.1:1 (there’s a famous Sioux named ”Red Cloud”); in Korean, white cloud wins 1.2:1, but four-leaf clover wins 2:1 over three-leaf clover.

Thereafter, Hal accurately points out:

”co-occurance frequencies of words definitely do not reflect co-occurance frequencies of things in the real world.”

But the mistake made by Hal is to assume language describes objective reality (”the real world”). Instead, I would argue that it describes social reality (”the social world”).

Black sheep in social reality. The higher occurence of ’black sheep’ tells us that in social reality, there is a concept called ’black sheep’ which is more common than the concept of white (or any color) sheep. People are using that concept, not to describe sheep, but as an abstract concept in fact describing other people (”she is the black sheep of the family”). Then, we can ask: Why is that? In what contexts is the concept used? And try to teach the machine its proper use through associations of that concept to other contexts (much like we teach kids when saying something is appropriate and when not). As a result, the machine may create a semantic web of abstract concepts which, if not leading to it understanding them, at least helps in guiding its usage of them.

We, the human. That’s assuming we want it to get closer to the meaning of the word in social reality. But we don’t necessarily want to focus on that, at least as a short-term goal. In the short-term, it might be more purposeful to understand that language is a reflection of social reality. This means we, the humans, can understand human societies better through its analysis. Rather than trying to teach machines to imputate data to avoid what we label an undesired state of social reality, we should use the outputs provided by the machine to understand where and why those biases take place. And then we should focus on fixing them. Most likely, technology plays only a minor role in that, although it could be used to encourage balanced view through a recommendation system, for example.

Conclusion. The ”correction of biases” is equivalent to burying your head in the sand: even if they magically disappeared from our models, they would still remain in the social reality, and through the connection of social reality and objective reality, echo in the everyday lives of people.